{"podcast":{"title":"Daily Paper Cast","slug":"daily-paper-cast-7079649","podcast_index_feed_id":7079649,"rss_url":"https://feeds.transistor.fm/daily-paper-cast-ai","website_url":"https://dailypapercast.transistor.fm/","image_url":"https://img.transistorcdn.com/IxaBeiMluxrMS9W9wB8hFMfmvH27KvwaSMzuhucupn0/rs:fill:0:0:1/w:1400/h:1400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS81Zjg1/YzRhODczMDU4MmE4/OGMwN2FiNDlmYzI2/MDliMi5qcGVn.jpg","author":"Jingwen Liang, Gengyu Wang","episode_count":1967,"summary":"We update every weekday to discuss highest-voted papers from Huggingface Daily Paper (https://huggingface.co/papers). Both the podcast scripts and audio are generated by AI. Feedback and suggestions are welcome! Email us: dailypapercast.ai@gmail.com Creator: Jingwen Liang, 3D ML, https://www.linkedin.com/in/jingwen-liang/ Gengyu Wang, LLM ML, http://wanggengyu.com Listen on: Spotify: https://open.spotify.com/show/21nrhmdaA8qoBiH8q03NXL Apple Podcast: https://podcasts.apple.com/us/podcast/daily-paper-cast/id1777620236 Cover Image by Kawen Kuang https://kawen.art","last_synced_at":"2026-06-14T04:17:49.264124+00:00","page_url":"https://stenobird.com/podcast/daily-paper-cast-7079649"},"episode":{"title":"Active Learners as Efficient PRP Rerankers","slug":"active-learners-as-efficient-prp-rerankers","published_at":"2026-05-21T04:37:55+00:00","page_url":"https://stenobird.com/podcast/daily-paper-cast-7079649/active-learners-as-efficient-prp-rerankers","show_page_url":"https://stenobird.com/podcast/daily-paper-cast-7079649","url":"https://share.transistor.fm/s/b44a223d","audio_url":"https://media.transistor.fm/b44a223d/3fb2e27f.mp3","summary":"🤗 Upvotes: 85 | cs.LG, cs.AI, cs.CL Authors: Jeremías Figueiredo Paschmann, Juan Kaplan, Francisco Nattero, Santiago Barron, Juan Wisznia, Luciano del Corro Title: Active Learners as Efficient PRP Rerankers Arxiv: http://arxiv.org/abs/2605.14236v2 Abstract: Pairwise Ranking Prompting (PRP) elicits pairwise preference judgments from an LLM, which are then aggregated into a ranking, usually via classical sorting algorithms. However, judgments are noisy, order-sensitive, and sometimes intransitive, so sorting assumptions do not match the setting. Because sorting aims to recover a full permutation, truncating it to meet a call budget does not produce a dependable top-K. We thus reframe PRP reranking as active learning from noisy pairwise comparisons and show that active rankers are drop-in replacements that improve NDCG@10 per call in the call-constrained regime. Our noise-robust framework also introduces a randomized-direction oracle that uses a single LLM call per pair. This approach converts systematic position bias into zero-mean noise, enabling unbiased aggregate ranking without the cost of bidirectional calls.","meta_description":"🤗 Upvotes: 85 | cs.LG, cs.AI, cs.CL Authors: Jeremías Figueiredo Paschmann, Juan Kaplan, Francisco Nattero, Santiago Barron, Juan Wisznia, Luciano del Cor…","key_points":[],"chapters":[],"topics":[],"duration_seconds":1419,"processing_state":"not_requested","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/daily-paper-cast-7079649/episodes/active-learners-as-efficient-prp-rerankers/transcription-requests","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/daily-paper-cast-7079649/active-learners-as-efficient-prp-rerankers.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}